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Nov 9, 2023
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Nov 9, 2023
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Talk by Kylie Huch of LBL. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: “What is consciousness?” is a highly general question, a much more quantifiable sub-question is “what are the common mathematical properties of learning-capable systems?”. In this talk we will examine a number of architectures implementing learning systems, the features determining the topology of the computational spaces they implement, and how information is represented and...
Topics: consciousness, neural networks, high-dimensional computing, learning, AI
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Oct 16, 2023
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Oct 16, 2023
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Talk by Saavan Patel, a recent PhD graduate of UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: As the demand for big data increases and the speed of traditional CPUs cannot keep pace, new computing paradigms and architectures are needed to meet the demands for our data hungry world. To keep pace with this, Ising Computing and probabilistic computing have emerged as a method to solve NP-Hard optimization problems (such as logistics, place and...
Topics: neuromorphic computing, optimization, asynchronous computing, parallel computing
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Sep 18, 2023
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Sep 18, 2023
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Talk given by Peter Loxley of the University of New England, Australia. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Reinforcement learning (RL) provides a general suite of algorithms for the approximate solution of many important and interesting optimal control tasks and sequential decision-making problems. At the heart of these problems is a complex tradeoff between exploration of new states and exploitation of known states. Most applications of RL...
Topics: AI, reinforcement learning, sparse coding
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Sep 18, 2023
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Sep 18, 2023
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Talk by Peter Loxley of the University of New England, Australia. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Reinforcement learning (RL) provides a general suite of algorithms for the approximate solution of many important and interesting optimal control tasks and sequential decision-making problems. At the heart of these problems is a complex tradeoff between exploration of new states and exploitation of known states. Most applications of RL algorithms...
Topics: AI, reinforcement learning
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Sep 18, 2023
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Sep 18, 2023
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Talk by Nick Alonso of UC Irvine. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed (non-i.i.d.) way. Further, synaptic plasticity in the brain depends only on information local to synapses. Deep networks, on the other hand, typically use non-local...
Topics: AI, Hopfield Network, Theoretical Neuroscience
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Jul 9, 2023
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Jul 9, 2023
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Talk by Zhaoping Li of the Max Planck Institute for Biological Cybernetics in Tuebingen. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Visual attention selects only a tiny fraction of visual input information for further processing and recognition. This makes humans blind to most visual inputs. Attentional selection starts at the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea (center of gaze) to selected visual...
Topics: human vision, theoretical neuroscience, area v1
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Jul 5, 2023
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Jul 5, 2023
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Talk by Pierre-Etienne Fiquet of New York University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: All organisms make temporal predictions, and their evolutionary fitness level generally scales with the accuracy of these predictions. In the context of visual perception, observer motion and continuous deformations of objects and textures structure the dynamics of visual signals, which allows for partial prediction of future inputs from past ones. Here,...
Topics: theoretical neuroscience, biological vision, machine learning
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Jun 15, 2023
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Jun 15, 2023
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Talk by Eshed Margalit of Stanford University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Virtually all cortical systems feature “functional organization”: the arrangement of neurons with specific functional properties into characteristic spatial arrangements. Functional organization is ubiquitous across systems and species, highly reproducible, and central to our understanding of cortical development and function, but we lack a unified framework...
Topics: theoretical neuroscience, primate visual system, deep learning, AI
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Apr 6, 2023
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Apr 6, 2023
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Talk by Ken Clarkson of IBM Research. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Hyperdimensional computing (HDC) is a biologically-inspired framework which represents symbols with high-dimensional vectors, and uses vector operations to manipulate them. The ensemble of a particular vector space and a prescribed set of vector operations (including one addition-like for "bundling" and one outer-product-like for "binding") form a vector...
Topics: hyperdimensional computing, vector symbolic architecture, theoretical capacity
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Mar 15, 2023
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Mar 15, 2023
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Talk by Adityanand Guntuboyina of UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: MARS is a popular method for nonparametric regression introduced by Friedman in 1991. MARS fits simple nonlinear and non-additive functions to regression data. We propose and study a natural LASSO variant of the MARS method. Our method is based on least squares estimation over a convex class of functions obtained by considering infinite-dimensional linear...
Topics: Mathematics, regression, non-linear regression
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Mar 6, 2023
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Mar 6, 2023
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Talk by Rishidev Chaudhuri of UC Davis. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Many networks in the brain are sparsely connected, and the brain eliminates connections during development and learning. How could the brain decide which synapses to prune? In a recurrent network, determining the importance of a connection between two neurons is a difficult computational problem, depending on the role that both neurons play and on all possible pathways of...
Topics: neural computation, neural network pruning, theoretical neuroscience
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Feb 24, 2023
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Feb 24, 2023
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Shayan Majidy
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Talk by Shayan Majidy who is a PhD student at the Institute for Quantum Computing, University of Waterloo advised by Raymond Laflamme. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Across thermodynamics, systems exchange quantities that are conserved globally, such as energy, particles, and electric charges. These quantities are represented by Hermitian operators, which have traditionally been assumed to commute with each other. However, noncommutation...
Topic: quantum thermodynamics
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Jan 27, 2023
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Jan 27, 2023
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Talk by Simon Del Pin of Colorlab, Norwegian University of Science and Technology. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Subjective ratings given by observers are a critical part of research in image and video quality assessment. In this talk, I first review different approaches to subjective ratings and identify potential pitfalls that are often overlooked. Using existing and newly collected data, I statistically demonstrate the non-linear use of...
Topics: subjective ratings, video quality assessment
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Nov 22, 2022
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Nov 22, 2022
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This Seminar has two talks. Talk by Alessandra Stella: Title: Multiple Overlapping Cell Assemblies Active During Motor Behavior Abstract: The cell assembly hypothesis postulates that information processing in the brain entails the repetitive co-activation of groups of neurons [1]. The activation of such assemblies would lead to spatio-temporal spike patterns (STPs) at the resolution of a few milliseconds. In order to test the cell assembly hypothesis, we searched for significant STPs in...
Topics: neuroscience, Cell assemblies, neural manifolds
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Jul 6, 2022
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Jul 6, 2022
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Talk by Kenny Schlegel of the Chemnitz University of Technology, Chemnitz, Germany. Given at the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Vector Symbolic Architectures (VSAs) combine a high-dimensional vector space with a set of carefully designed operators in order to perform symbolic computations with large numerical vectors. Major goals are the exploitation of their representational power and ability to deal with fuzziness and ambiguity. The basis of a VSA are...
Topics: hyperdimensional computing, Vector Symbolic Architectures, computer vision, AI
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Jun 16, 2022
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Jun 16, 2022
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Talk by Yvette Fisher of UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: In the Drosophila brain, head direction neurons form a network whose activity tracks the angular position of the fly using both self-movement and visual inputs. Our recent work seeks to understand the circuit and synaptic mechanisms that allow these internal and external signals to be seamlessly combined into a coherent sense of direction. Using in vivo electrophysiology...
Topics: theoretical neuroscience, drosophila, neuroscience, visual neuroscience
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May 19, 2022
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May 19, 2022
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Talk by Dileep George of Vicarious AI. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Place fields in the hippocampus show a variety of remapping phenomena in response to environmental changes. These remapping phenomena get characterized in terms of different types of coding of spatial information -- object vector cells, landmark vector cells, distance coding, etc. But what if these phenomena are side effects of mapping hippocampal neuronal responses on...
Topics: hippocampus, AI, learning, theoretical neuroscience
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Mar 31, 2022
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Mar 31, 2022
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Slides for this talk . Talk by Rina Panigrahy of Google. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: What is a reasonable architecture for an algorithmic view of the mind? Is it akin to a single giant deep network or is it more like several small modules connected by some graph? How is memory captured — is it some lookup table? Take a simple event like meeting someone over coffee — how would your mind remember who the person was, what was discussed?...
Topics: theoretical neuroscience, neural networks, knowledge graph, psychology, memory
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Mar 17, 2022
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Mar 17, 2022
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Talk given by Daniel Toker of UCLA. Given via Zoom to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory...
Topics: consciousness, neuroscience cortical oscillations
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Mar 4, 2022
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Mar 4, 2022
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Talk given by Miguel Carreira-Perpinan of UC Merced. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Decision trees, one of the oldest statistical and machine learning models, are widely used as interpretable models and as the basis of random and boosted forests. However, their true power remains underexploited. The reason is well known: they involve a difficult, nonconvex, nondifferentiable optimization problem, caused by the discrete nature of the tree...
Topics: AI, decision trees, machine learning, neural networks
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Feb 24, 2022
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Feb 24, 2022
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Talk given by Arturo Deza from MIT. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Recent work suggests that feature constraints in the training datasets of deep neural networks (DNNs) drive robustness to adversarial noise (Ilyas et al., 2019). The representations learned by such adversarially robust networks have also been shown to be more human perceptually-aligned than non-robust networks via image manipulations (Santurkar et al., 2019, Engstrom et al.,...
Topics: Deep Neural Networks, Visual perception, human vision
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Dec 10, 2021
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Dec 10, 2021
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Talk by Thomas Langlois of Princeton University and UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: The visual system must constantly generate meaning by combining noisy sensory information with efficient internal representations. However, as essential as these hidden representations are in shaping many aspects of cognition and behavior, they can be difficult to measure directly. In my work, I apply adaptive sampling techniques based on serial...
Topics: neuroscience, vision, visual memory
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Dec 3, 2021
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Dec 3, 2021
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Talk given by Yi Ma of EECS Dept at UC Berkeley to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Note about video files and audio quality: There are three video files. The first video file is from the Zoom recording. Audio for that was recorded using an mic that was not directly attached to the speaker. The second two videos were recorded from a camcorder in the room. The audio for those was from a mic that the speaker was wearing which often made the audio quality a...
Topics: learning, statistics, AI, memory
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Nov 19, 2021
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Nov 19, 2021
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Talk by Joscha Bach of the Intel labs Cognitive Computing Group. Given at the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: What is the most important unsolved problem in Artificial Intelligence? It may well be the problem of creating a unified model of the universe, to which all observations of the system can be related to. This requires representations that are universal and dynamic, combine tacit and propositional knowledge, and can be grounded in predictive models...
Topics: AI, Consciousness, attention
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Oct 29, 2021
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Oct 29, 2021
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Talk by Jack Kendall of Rain Neuromorphics. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Analog compute-in-memory (CIM) architectures for low-power neural networks have recently been shown to achieve excellent compute efficiency and high accuracy, comparable to software-based deep neural networks. However, two primary limitations prevent them from reaching their potential: 1) resistive crossbars have difficulty scaling to large, sparse networks; and 2)...
Topics: analog neural networks, machine learning, AI
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Apr 21, 2021
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Apr 21, 2021
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Talk by Jeff Hawkins and Subutai Ahmad of Numenta, Inc. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: The neocortex exhibits a detailed architecture that is largely preserved across different functional areas and between species. This has led to the idea of a common cortical algorithm that underlies all aspects of perception, language, and thought. Whether such a common algorithm exists and what it could be has been debated for decades. Our team has...
Topics: theoretical neuroscience, cortex
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Apr 1, 2021
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Apr 1, 2021
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Talk given by Stéphane Deny of Facebook AI Research. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley via Zoom. There are two video files because due to technical problems, there was a break in the recording during the discussion after the main talk. Abstract: Why is visual information transmitted through many parallel channels in the optic nerve, with each channel encoding a different feature-map of the visual scene? Why do neurons in the retina prefer disk-shaped...
Topics: computational neuroscience, vision, retina, machine learning, AI
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Feb 11, 2021
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Feb 11, 2021
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Lecture 29 (Dec 8) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is N eural oscillations and synchrony Binding by synchrony Coupled oscillator networks Computing with waves This is the final lecture of the class.
Topics: theoretical neuroscience, neural oscillations, synchrony
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Feb 11, 2021
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Feb 11, 2021
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Lecture 28 (Dec 3) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is hyperdimensional computing (lecture by Spencer Kent and Navneedh Maudgalya; continues lecture 27 given Dec 1) Local vs. distributed representation Binding, bundling and sequencing Computing in superposition
Topics: theoretical neuroscience, hyperdimensional computing
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Feb 11, 2021
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Feb 11, 2021
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Lecture 27 (Dec 1) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hyperdimensional computing Local vs. distributed representation Binding, bundling and sequencing Computing in superposition
Topics: theoretical neuroscience, hyperdimensional computing
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Feb 11, 2021
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Feb 11, 2021
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Lecture 26 (Nov 24) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Reinforcement learning and basal ganglia (lecture by Sophia Sanborn)
Topics: theoretical neuroscience, reinforcement learning, basal ganglia
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Feb 11, 2021
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Feb 11, 2021
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Lecture 25 (Nov 19) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Supervised learning (and cerebellum) Delta learning rule and back-propagation Supervised learning in cerebellum Kanerva’s Sparse Distributed Memory Model
Topics: theoretical neuroscience, supervised learning, cerebellum, sparse distributed memory, delta...
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Feb 11, 2021
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Feb 11, 2021
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Lecture 24 (Nov 17) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Perception-action loop Active perception Representations for perception vs. action Thalamocortical loop as sensorimotor loop
Topics: theoretical neuroscience, perception-action loop
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Feb 11, 2021
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Feb 11, 2021
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Lecture 23 (Nov 12) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Project mini-presentations
Topic: theoretical neuroscience
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Feb 11, 2021
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Feb 11, 2021
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Lecture 22 (Nov 10) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hierarchical models and invariance (Lecture is by Sophia Sanborn; topic continues lecture 21 given Nov 5) Learning of higher-order structure, part-whole relationships Models of invariance Three-way interactions/dynamic routing Complex cells, power spectrum vs. bispectrum
Topics: theoretical neuroscience, hierarchical models
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Feb 11, 2021
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Lecture 21 (Nov 5) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Hierarchical models and invariance Learning of higher-order structure, part-whole relationships Models of invariance Three-way interactions/dynamic routing Complex cells, power spectrum vs. bispectrum
Topics: theoretical neuroscience, hierarchical models
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Feb 11, 2021
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Lecture 20 (Nov 3) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference – denoising, dynamical models Denoising with a sparse coding prior Dynamical models
Topics: theoretical neuroscience, inference, dynamic models
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Feb 11, 2021
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Lecture 19 (Oct 29) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Probabilistic models Learning and inference in generative models Boltzmann machines Restricted Boltzmann machines and Energy-based models
Topics: theoretical neuroscience, generative models, boltzmann machines
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Feb 11, 2021
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Lecture 18 (Oct 27) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Attractor dynamics (continues lecture 17 given Oct 22) Hopfield networks, memories as ‘basis of attraction’ Line attractors and `bump circuits’
Topics: theoretical neuroscience, Hopfield networks, attractor dynamics
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Feb 11, 2021
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Lecture 17 (Oct 22) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Attractor dynamics Hopfield networks, memories as ‘basis of attraction’ Line attractors and `bump circuits’
Topics: theoretical neuroscience, hopfield networks, attractor dynamics
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Feb 11, 2021
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Lecture 16 (Oct 20) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics are Organization, topography (continues lecture 15 given Oct 15) Horizontal connections Self-organizing maps Manifold models
Topics: theoretical neuroscience, self-organizing maps
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Feb 11, 2021
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Lecture 15 (Oct 15) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics are Organization, topography Horizontal connections Self-organizing maps Manifold models
Topics: theoretical neuroscience, self-organizing maps
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Feb 11, 2021
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Lecture 14 (Oct 13) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference (continues lectures 12 & 13 given Oct 6 & Oct 8) LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1
Topics: theoretical neuroscience, sparse coding, hebbian learning
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Feb 11, 2021
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Lecture 13 (Oct 8) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference (continuation of lecture 12 given Oct 6) LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1
Topics: theoretical neuroscience, sparse coding, hebbian learning
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Lecture 12 (Oct 6) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Inference LGN and Cortex Overcomplete representation in primary visual cortex Hebbian learning and PCA Sparse coding model of V1
Topics: theoretical neuroscience, sparse coding, hebbian learning
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Lecture 11 (Oct 1) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural coding in Lateral Geniculate Nucleus (LGN) (Lecture given by Fritz Sommer)
Topics: theoretical neuroscience, Lateral Geniculate Nucleus, neural coding
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Feb 11, 2021
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Lecture 10 (Sept 29) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Starburst amacrine cells and the computation of motion (lecture by Rowland Taylor)
Topics: retina, motion detection, amacrine cells, theoretical neuroscience
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Lecture 9 (Sept 24) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Auditory coding Cochlea and auditory nerve Time-frequency analysis Phase and amplitude coding by spikes ICA of natural sound
Topics: theoretical neuroscience, auditory coding
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Feb 10, 2021
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Lecture 8 (Sept 22) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Retinal tiling Cone tiling Subdivision of labor by midget and parasol RGC’s Foveated sampling
Topics: theoretical neuroscience, retina
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Feb 10, 2021
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Lecture 7 (Sept 17) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural coding (continues lecture 6 given Sept 15) Spiking neuron models Signal compression in retina: theory of retinal whitening
Topics: theoretical neuroscience, neural coding
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Feb 10, 2021
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Lecture 6 (Sept 15) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural coding: Spiking neuron models Signal compression in retina: theory of retinal whitening
Topics: theoretical neuroscience, retina, neural coding
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Lecture 5 (Sept 10) of UC Berkeley Vision Science 265: Neural Computation, taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Physics of computation Horizontal cells and lateral inhibition; Optimal pooling by bipolar cells Analog VLSI and silicon retina
Topics: theoretical neuroscience, retina, lateral inhibition
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Lecture 4 (Sept 8) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Signal detection and amplification Computing with chemistry/allostery Phototransduction
Topics: theoretical neuroscience, phototransduction, signal detection
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Lecture 3 (Sept 3) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topic is Neural mechanisms and models Membrane equation, compartmental model of a neuron Shunting inhibition, NMDA, dendritic nonlinearities Perceptron model
Topics: theoretical neuroscience, neuron models, perceptron model, neuroscience
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Lecture 2 (Sept 1) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ This lecture (in two video files) is about Brains Mammalian brain organization Insect and spider brains
Topics: neuroscience, brain organization
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Lecture 1 (Aug 27) of UC Berkeley Vision Science 265: Neural Computation taught by Bruno Olshausen in Fall 2020. Syllabus for the course is at: https://redwood.berkeley.edu/courses/vs265/ Topics is Introduction Theory and modeling in neuroscience Goals of AI/machine learning vs. theoretical neuroscience
Topics: theoretical neuroscience, machine learning
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Dec 29, 2020
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Dec 29, 2020
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Lecture by Spencer Kent and Navneedh Maudgalya in the "Neural Computation" course (Vision Science 265) Fall semester 2020.
Topics: neural networks, artificial intelligence, theoretical neuroscience, vector symbolic architectures,...
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Dec 29, 2020
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Dec 29, 2020
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Talk by Denis Kleyko of the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Many neural network models have been successful at classification problems, but their operation is still treated as a black box. In the talk, I will describe a theory for one-layer perceptrons that can predict performance on classification tasks. This theory is a generalization of an existing theory for predicting the performance of some Echo State Networks and connectionist models for symbolic...
Topics: theoretical neuroscience, perceptron, neural networks, machine learning, artificial intelligence
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Dec 19, 2020
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Dec 19, 2020
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Dissertation talk by Spencer Kent of the Redwood Center for Theoretical Neuroscience at UC Berkeley, given to the Redwood Center. Abstract: Perception and cognition are teeming with signals and concepts that interact in a multiplicative way, and it is largely this pattern of combination that generates the awesome variability and complexity of our physical and mental worlds. Human brains are prodigious in contending with such complexity, in part due to their ability to factor concepts into more...
Topics: theoretical neuroscience, vector symbolic architectures, high-dimensional computing, neural...
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Dec 7, 2020
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Dec 7, 2020
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Talk by Pentti Kanerva given at a meeting of The Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: The standard model of computing treats variables very differently from their values. Variables are addresses to memory, and values are the contents of the addressed memory locations. This model of computing has proved to be extremely successful but also poor for modeling the brain's computing. Neither does it look like what we see happening in brains. Rather,...
Topics: high dimensional computing, theoretical neuroscience
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Dec 7, 2020
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Dec 7, 2020
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This item has been deleted and replaced by: https://archive.org/details/Redwood_Center_2020_02_07_Pentti_Kanerva
Topic: deleted item
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Dec 7, 2020
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Dec 7, 2020
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Talk by Friedrich Sommer of UC Berkeley. Given at a meeting of the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Connectionism is a movement in Psychology that began in the 1980ies to understand cognition using neural network models operating on distributed representations. Connectionism not only greatly advanced traditional neural networks, i.e. working out error back propagation, it also identified their limitations. Importantly, traditional neural networks lack...
Topics: vector symbolic architecture, theoretical neuroscience
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Nov 18, 2020
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Nov 18, 2020
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Talk by Jacob Yates of the University of Maryland, Department of Biology. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Most of the core computational concepts in visual neuroscience come from studies using anesthetized or fixating subjects. Although this approach is designed to maximize experimental control – by stabilizing the subject’s gaze – the net result is that the most commonly used visual stimulus is a fixation point and little is known...
Topics: primate vision system, eye movements, neurophysiology
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Nov 14, 2020
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Nov 14, 2020
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Talk by Zoran Tiganj of Indiana University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Building artificial agents that can mimic human learning and reasoning has been a longstanding objective in artificial intelligence. I will discuss some of the empirical data and computational models from neuroscience and cognitive science that could help us advance towards this goal. Specifically, I will talk about the importance of structured representations of...
Topics: cognitive maps, theoretical neuroscience, learning
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Oct 27, 2020
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Oct 27, 2020
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Talk by Jeremy England of the Department of Physics, Georgia Institute of Technology. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley, via Zoom. Abstract: Self-organization is frequently observed in active collectives, from ant rafts to molecular motor assemblies. General principles describing self-organization away from equilibrium have been challenging to identify. We offer a unifying framework that models the behavior of complex systems as largely random, while...
Topic: self-organization
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Feb 12, 2020
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Feb 12, 2020
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Talk by Ruben Coen-Cagli, of the Albert Einstein College of Medicine. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract A central goal of vision science is to understand the principles underlying the perception and cortical encoding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much...
Topics: visual cortex, theoretical neuroscience, divisive normalization
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Feb 5, 2020
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Feb 5, 2020
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Talk by Victor Boutin of the Institute of Neuroscience of la Timone (INT), Aix-Marseille University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Building models to efficiently represent images is a central problem in the machine learning community. The brain and especially the visual cortex, has long find economical and robust solutions to solve such a problem. At the local scale, Sparse Coding is one of the most successful framework to model neural...
Topics: sparse coding, predictive coding, visual system
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Jan 29, 2020
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Jan 29, 2020
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Talk by Rudiger von der Heydt from Johns Hopkins University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Image understanding is often conceived as a hierarchical process with many levels where complexity and invariance of object selectivity gradually increase with level in the hierarchy. In contrast, neurophysiological studies have shown that figure-ground organization and border ownership coding, which imply understanding of the object structure of...
Topics: neuroscience, vision, visual cortex, object recognition
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Jan 18, 2020
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Jan 18, 2020
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In this talk, I will first present a signal representation framework called the Sparse Manifold Transform that combines key ideas from sparse coding, manifold learning, and slow feature analysis. It turns non-linear transformations in the primary sensory signal space into linear interpolations in a representational embedding space while maintaining approximate invertibility. The sparse manifold transform is an unsupervised and generative framework that explicitly and simultaneously models the...
Topics: sparse coding, theoretical neuroscience
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Dec 12, 2019
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Dec 12, 2019
by
Berkeley_ICBS_2019_fall_retreat
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Talk by Stan Klein at the UC Berkeley ICBS 2019 Fall retreat.
Topics: neuroscience, evoked potentials
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Dec 12, 2019
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Dec 12, 2019
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Berkeley_ICBS_2019_fall_retreat
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Talk by Jonathan Tsay at the UC Berkeley ICBS Fall retreat.
Topics: neuroscience, vision
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Nov 22, 2019
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Nov 22, 2019
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Talk by Kara Emery, from the University of Nevada, Reno. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract An important goal of vision science is to understand the coding strategies underlying the representation of visual information. I will describe experiments and analyses where we have explored these coding strategies using two different approaches. In the first approach, we factor-analyzed individual differences in observers’ color judgments to reveal...
Topics: theoretical neuroscience, color vision, neural coding
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Nov 21, 2019
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Nov 21, 2019
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Talk by Leah Krubitzer of UC Davis. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Forelimb morphology and use in mammals is extraordinarily diverse. Evolution has produced wings, flippers, hooves, paws and hands which are specialized for a variety of behaviors such as flying, swimming and grasping to name a few. While there is a wealth of data in human and non-human primates on the role of motor cortex and posterior parietal cortical areas in reaching...
Topics: motor cortex, theoretical neuroscience
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Nov 8, 2019
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Nov 8, 2019
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Talk by David Freedman, Professor, Department of Neurobiology, The University of Chicago. Given at the ICBS Colloquium: Friday, Nov. 8, 2019 at UC Berkeley. Abstract Humans and other advanced animals have a remarkable ability to interpret incoming sensory stimuli and plan task-appropriate behavioral responses. This talk will present parallel experimental and computational approaches aimed at understanding how visual feature encoding in upstream sensory cortical areas is transformed across...
Topics: theoretical neuroscience, neural networks
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Nov 6, 2019
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Nov 6, 2019
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Talk by Aaron Milstein from the Stanford University School of Medicine, Dept. of Neurosurgery. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract How do neural circuits in the brain accomplish rapid learning? When foraging for food in a previously unexplored environment, animals store memories of landmarks based on as few as one single view. Also, animals remember landmarks and navigation decisions that eventually lead to food, which requires that the brain...
Topics: learning, hippocampus, dendrites
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Sep 25, 2019
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Sep 25, 2019
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Talk by Gary Marcus of NYU. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Artificial intelligence has a trust problem. We are relying on A.I. more and more, but it hasn’t yet earned our confidence. Despite the intense recent hype surrounding AI, no current AI system remotely approaches the flexibility of human intelligence; as I will show, even the ability to read at grade-school level eludes current approaches. Building on my recent synthesis...
Topics: AI, deep learning, cognitive psychology
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Sep 5, 2019
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Sep 5, 2019
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Talk by Pam Reinagel from UC San Diego. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract In rapid sensory decision-making, the time taken to choose and the accuracy of the choice are related in three distinct ways. First, it takes more time to assess noisy signals, so decisions about weak sensory stimuli are slower, as well as less accurate. Second, for any given stimulus strength, adopting an overall policy of higher stringency will make decisions slower, but...
Topics: theoretical neuroscience, decision making
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Sep 3, 2019
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Sep 3, 2019
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Talk by Peter Loxley from the University of New England, Armidale, Australia. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Dynamic Programming with Sparse Codes: Investigating a New Computational Role for Sparse Representations of Natural Image Sequences Dynamic programming (DP) is a general algorithmic approach used in optimal control and Markov decision processes that balances desire for low present costs with undesirability of high future costs when...
Topics: sparse coding, object tracking, machine learning, AI
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Aug 5, 2019
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Aug 5, 2019
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Talk by David Zipser of the Redwood Center for Theoretical Neuroscience. Given at the Friday lab meeting for the Redwood Center. Abstract Thirty years ago we developed learning algorithms for recurrent neural networks. Since then I have trained many recurrent networks to simulate a variety of dynamical systems usually with some relevance to neuroscience. A drawback to this approach is the requirement for lots of data from the system being simulated. This is often not available form real...
Topics: dynamical systems, theoretical neuroscience
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Jul 13, 2019
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Jul 13, 2019
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Talk by Wujie Zhang from the Yartsev Lab at UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Social interaction is fundamental to our everyday life and that of diverse animals. When two animals interact, they behave in different ways. Thus, to get a full picture of the neural activity underlying each interaction, we need to record from the brains of both animals at the same time. We do so in a highly social mammal, the Egyptian fruit bat, using...
Topics: neuroscience, social interactions, LFP
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Jul 13, 2019
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Jul 13, 2019
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Talk by Antoni Chan and Janet Hsiao from the City University of Hong Kong, University of Hong Kong. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract We propose a hierarchical EM algorithm for simplifying a finite mixture model into a reduced mixture model with fewer mixture components. The reduced model is obtained by maximizing a variational lower bound of the expected log-likelihood of a set of virtual samples. We develop three applications for our mixture...
Topics: Eye movements, hidden markov model, face recognition
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9.0
Jun 12, 2019
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Jun 12, 2019
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This item is no longer available.
Topics: vision, perception, line drawings
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Jun 5, 2019
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Jun 5, 2019
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Talk by Paul F.M.J. Verschure from the Barcelona Institute of Science and Technology. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Motor control is usually seen as the result of a gradual replacement of feedback by feedforward control. The perceptual states that inform this process are considered to be defined through qualitatively different processes giving rise to the classical distinction between perceptual and behavioral learning. We have addressed...
Topic: theoretical neuroscience
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May 15, 2019
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May 15, 2019
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Talk by Bill Softky, Visiting scholar, Bioengineering Department, Stanford University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Note: the slides for the talk are in the Powerpoint file which can be downloaded using the link on this page. Abstract It is a truth not yet universally acknowledged that a self-regulating system which is stable in one environment can become unstable when the environment changes. This truth is called homeostatic fragility. ...
Topics: screen addiction, theoretical neuroscience
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May 2, 2019
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May 2, 2019
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Talk by Tianshi Wang from EECS, UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Over the last few years, there has been considerable interest in Ising machines, ie, analog hardware for solving difficult (NP hard/complete) computational problems effectively. We present a new way to make Ising machines using networks of coupled self-sustaining nonlinear oscillators. Our scheme is theoretically rooted in a novel result that connects the phase...
Topics: NP Hard, dynamics, oscillator networks
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Apr 24, 2019
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Apr 24, 2019
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Talk by David Rolnick from the University of Pennsylvania. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract It is well-known that the expressivity of a neural network depends on its architecture, with deeper networks expressing more complex functions. For ReLU networks, which are piecewise linear, the number of distinct linear regions is a natural measure of expressivity. It is possible to construct networks for which the number of linear regions grows...
Topics: deep learning, machine learning
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Apr 17, 2019
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Apr 17, 2019
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Talk by Gautam Agarwal, from the Champalimaud Institute of the Unknown, Lisbon Portugal. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Learning a complex skill requires traversing a potentially enormous search space. While reinforcement learning (RL) algorithms can approach human levels of performance in complex tasks, they require much more training to do so. This may be because humans constrain search problems with prior knowledge, allowing them to more rapidly...
Topic: learning
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Apr 12, 2019
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Apr 12, 2019
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Talk by Risi Kondor of the University of Chicago. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Deep neural networks have proved to be extremely effective in image recognition, machine translation, and a variety of other data centered engineering tasks. However, some other domains, such as learning to model physical systems requires a more careful examination of how neural networks reflect symmetries. In this talk we give an overview of recent developments...
Topics: Neural networks, machine learning
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Mar 22, 2019
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Mar 22, 2019
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Talk by John Baez from UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract If biology is the study of self-replicating entities, and we want to understand the role of information, it makes sense to see how information theory is connected to the ‘replicator equation’ — a simple model of population dynamics for self-replicating entities. The relevant concept of information turns out to be the information of one probability distribution relative...
Topics: evolution, natural selection, theoretical biology
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Mar 11, 2019
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Mar 11, 2019
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Talk by Patrick Kaifosh, PhD, CSO & Co-Founder, CTRL-labs. Given at the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract As the nervous system's evolved output, spinal motor neuron activity is from an evolutionary perspective a natural source of signals for a neural interface. Furthermore, the amplification of these signals by muscle fibers allows them to be measured non-invasively with surface electromyography (sEMG). CTRL-labs has developed a wireless wearable...
Topics: brain machine interface, surface electromyography
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Mar 2, 2019
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Mar 2, 2019
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Talk by Andrew Glennerster from the University of Reading. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Note: Slides for the talk are in the PDF and POWERPOINT files included here and available for download. The POWERPOINT file uses a Reading font so it may not display perfectly if that font is not available, but it does include the animations. The PDF should always display correctly, but it does not include the animations. Abstract It is clear that animals do...
Topics: animal vision, theoretical neuroscience
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Feb 20, 2019
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Feb 20, 2019
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Talk by Steve Piantadosi, UC Berkeley, Dept. of Psychology. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract I’ll present an approach from mathematical logic which shows how sub-symbolic dynamics may give rise to higher-level cognitive representations of structures, systems of knowledge, and algorithmic processes. This approach posits that learners posses a system for e xpressing isomorphisms with which they create mental models with arbitrary dynamics. The...
Topics: theoretical neuroscience, cognition, knowledge representation
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Feb 15, 2019
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Feb 15, 2019
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Talk by James Cooke from UCL. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Perception has long been considered to be an inference operation in which internal models of the sensory environment are constructed through experience and are subsequently used in order to assign sensory stimuli to appropriate object categories. When confronted with noisy or incomplete sense data, these internal models are used to identify the most likely object category that might...
Topics: auditory perception, neuroscience
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Feb 6, 2019
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Feb 6, 2019
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Talk by Stella Yu from ETH Zurich. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Computer vision has advanced rapidly with deep learning, achieving super-human performance on a few recognition benchmarks. At the core of the state-of-the-art approaches for image classification, object detection, and semantic/instance segmentation is sliding-window classification, engineered for computational efficiency. Such piecemeal analysis of visual perception often...
Topics: computer vision, machine learning, deep learning
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Feb 5, 2019
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Feb 5, 2019
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Talk by Yulia Sandamirskaya from ETH Zurich. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Activity of neuronal populations in several cortical regions can be described by a Dynamic Neural Field (DNF) equation. A DNF is a continuous in time and in space activation function defined over a metric space spanned over perceptual (e.g., color, retinal location, orientation) or motor (e.g., orientation of the head, direction of movement) dimensions, in which...
Topics: neural networks, Dynamic Neural Field
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Jan 30, 2019
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Jan 30, 2019
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Talk by John Co-Reyes from UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: Sparse reward and long horizon tasks are among the most interesting yet challenging problems to solve in reinforcement learning. I will discuss recent work leveraging representation learning to tackle these sets of problems. We present a novel model which learns a latent representation of low-level skills by embedding trajectories with a variational autoencoder. Skills...
Topic: reinforcement learning
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Jan 17, 2019
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Jan 17, 2019
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Talk by Carlos Brody from Princeton University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: I will describe studies of the neural bases of cognitive processes. Rodents, mostly rats, are trained to perform behaviors that lend themselves to quantitative modeling that can help identify and assess specific cognitive processes, such as decision-making, short-term memory, planning, and executive control. With these well-quantified behaviors in hand, we then...
Topics: computational neuroscience, decision-making
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Nov 8, 2018
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Nov 8, 2018
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Talk by Shiming Tang from Peking University. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract My lab focuses on the neural mechanism of visual object recognition and developing techniques for neuronal circuit mapping. We have established long-term two-photon imaging in awake monkeys — the first and critical step toward comprehensive circuit mapping — to identify single neuron functions. We have systematically characterized the V1 neuronal responses with...
Topics: visual cortex, V1
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Nov 8, 2018
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Nov 8, 2018
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Talk by Gamaleldin Elsayed from Google Brain. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract Machine learning models are vulnerable to adversarial examples: small changes to images can cause computer vision models to make mistakes such as identifying a school bus as an ostrich. However, it is still an open question whether humans are prone to similar mistakes. Here, we address this question by leveraging recent techniques that transfer adversarial examples...
Topic: machine learning
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Oct 17, 2018
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Oct 17, 2018
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Talk by Peter Bickel from UC Berkeley. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley. Abstract: (Joint work with Gil Kur and Boaz Nadler) The notion of projection pursuit according to Huber (1985) appeared first in work of J.B. Kruskal (1969,1972) and was implemented and popularized by Friedman and Tukey (1974). Key papers are Huber (1985) and Diaconis and Freedman (1984). The notion, crudely, according to Huber, is that a p dimensional sample can be studied...
Topic: dimensionality reduction
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